# RaggedExperiment

Martin Morgan1 and Marcel Ramos1

1Roswell Park Comprehensive Cancer Center, Buffalo, NY

#### 1 May 2024

# 1 Introduction

The RaggedExperiment package provides a flexible data representation for copy number, mutation and other ragged array schema for genomic location data. It aims to provide a framework for a set of samples that have differing numbers of genomic ranges.

The `RaggedExperiment` class derives from a `GRangesList` representation and provides a semblance of a rectangular dataset. The row and column dimensions of the `RaggedExperiment` correspond to the number of ranges in the entire dataset and the number of samples represented in the data, respectively.

# 2 Installation

    if (!require("BiocManager"))
        install.packages("BiocManager")
    BiocManager::install("RaggedExperiment")

Loading the package:

    library(RaggedExperiment)
    library(GenomicRanges)

# 3 Citing RaggedExperiment

Your citations are crucial in keeping our software free and open source. To cite our package see the citation (Ramos et al. (2023)) in the Reference section. You may also browse to the publication at the link here.

# 4 `RaggedExperiment` class overview

A schematic showing the class geometry and supported transformations of the `RaggedExperiment` class is show below. There are three main operations for transforming the `RaggedExperiment` representation:

1. `sparseAssay`
2. `compactAssay`
3. `qreduceAssay`

Figure 1: RaggedExperiment object schematic
Rows and columns represent genomic ranges and samples, respectively. Assay operations can be performed with (from left to right) compactAssay, qreduceAssay, and sparseAssay.

# 5 Constructing a `RaggedExperiment` object

We start with a couple of `GRanges` objects, each representing an individual sample:

    sample1 <- GRanges(
        c(A = "chr1:1-10:-", B = "chr1:8-14:+", C = "chr2:15-18:+"),
        score = 3:5)
    sample2 <- GRanges(
        c(D = "chr1:1-10:-", E = "chr2:11-18:+"),
        score = 1:2)

Include column data `colData` to describe the samples:

    colDat <- DataFrame(id = 1:2)

## 5.1 Using `GRanges` objects

    ragexp <- RaggedExperiment(
        sample1 = sample1,
        sample2 = sample2,
        colData = colDat
    )
    ragexp

    ## class: RaggedExperiment
    ## dim: 5 2
    ## assays(1): score
    ## rownames(5): A B C D E
    ## colnames(2): sample1 sample2
    ## colData names(1): id

## 5.2 Using a `GRangesList` instance

    grl <- GRangesList(sample1 = sample1, sample2 = sample2)
    RaggedExperiment(grl, colData = colDat)

    ## class: RaggedExperiment
    ## dim: 5 2
    ## assays(1): score
    ## rownames(5): A B C D E
    ## colnames(2): sample1 sample2
    ## colData names(1): id

## 5.3 Using a `list` of `GRanges`

    rangeList <- list(sample1 = sample1, sample2 = sample2)
    RaggedExperiment(rangeList, colData = colDat)

    ## class: RaggedExperiment
    ## dim: 5 2
    ## assays(1): score
    ## rownames(5): A B C D E
    ## colnames(2): sample1 sample2
    ## colData names(1): id

## 5.4 Using a `List` of `GRanges` with metadata

Note: In cases where a `SimpleGenomicRangesList` is provided along with accompanying metadata (accessed by `mcols`), the metadata is used as the `colData` for the `RaggedExperiment`.

    grList <- List(sample1 = sample1, sample2 = sample2)
    mcols(grList) <- colDat
    RaggedExperiment(grList)

    ## class: RaggedExperiment
    ## dim: 5 2
    ## assays(1): score
    ## rownames(5): A B C D E
    ## colnames(2): sample1 sample2
    ## colData names(1): id

# 6 Accessors

## 6.1 Range data

    rowRanges(ragexp)

    ## GRanges object with 5 ranges and 0 metadata columns:
    ##     seqnames    ranges strand
    ##        <Rle> <IRanges>  <Rle>
    ##   A     chr1      1-10      -
    ##   B     chr1      8-14      +
    ##   C     chr2     15-18      +
    ##   D     chr1      1-10      -
    ##   E     chr2     11-18      +
    ##   -------
    ##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

## 6.2 Dimension names

    dimnames(ragexp)

    ## [[1]]
    ## [1] "A" "B" "C" "D" "E"
    ##
    ## [[2]]
    ## [1] "sample1" "sample2"

## 6.3 `colData`

    colData(ragexp)

    ## DataFrame with 2 rows and 1 column
    ##                id
    ##         <integer>
    ## sample1         1
    ## sample2         2

# 7 Subsetting

## 7.1 by dimension

Subsetting a `RaggedExperiment` is akin to subsetting a `matrix` object. Rows correspond to genomic ranges and columns to samples or specimen. It is possible to subset using `integer`, `character`, and `logical` indices.

## 7.2 by genomic ranges

The `overlapsAny` and `subsetByOverlaps` functionalities are available for use for `RaggedExperiment`. Please see the corresponding documentation in `RaggedExperiment` and `GenomicRanges`.

# 8 *Assay functions

`RaggedExperiment` package provides several different functions for representing ranged data in a rectangular matrix via the `*Assay` methods.

## 8.1 sparseAssay

The most straightforward matrix representation of a `RaggedExperiment` will return a matrix of dimensions equal to the product of the number of ranges and samples.

    dim(ragexp)

    ## [1] 5 2

    Reduce(`*`, dim(ragexp))

    ## [1] 10

    sparseAssay(ragexp)

    ##   sample1 sample2
    ## A       3      NA
    ## B       4      NA
    ## C       5      NA
    ## D      NA       1
    ## E      NA       2

    length(sparseAssay(ragexp))

    ## [1] 10

### 8.1.1 Support for sparse matrix output

We provide sparse matrix representations with the help of the `Matrix` package. To obtain a sparse representation, the user can use the `sparse = TRUE` argument.

    sparseAssay(ragexp, sparse = TRUE)

    ## 5 x 2 sparse Matrix of class "dgCMatrix"
    ##   sample1 sample2
    ## A       3       .
    ## B       4       .
    ## C       5       .
    ## D       .       1
    ## E       .       2

This representation is of class `dgCMatrix` see the `Matrix` documentation for more details.

## 8.2 compactAssay

Samples with identical ranges are placed in the same row. Non-disjoint ranges are not collapsed.

    compactAssay(ragexp)

    ##              sample1 sample2
    ## chr1:8-14:+        4      NA
    ## chr1:1-10:-        3       1
    ## chr2:11-18:+      NA       2
    ## chr2:15-18:+       5      NA

Similarly, to `sparseAssay` the `compactAssay` function provides the option to obtain a sparse matrix representation with the `sparse = TRUE` argument. This will return a `dgCMatrix` class from the `Matrix` package.

    compactAssay(ragexp, sparse = TRUE)

    ## 4 x 2 sparse Matrix of class "dgCMatrix"
    ##              sample1 sample2
    ## chr1:1-10:-        3       1
    ## chr1:8-14:+        4       .
    ## chr2:15-18:+       5       .
    ## chr2:11-18:+       .       2

## 8.3 disjoinAssay

This function returns a matrix of disjoint ranges across all samples. Elements of the matrix are summarized by applying the `simplifyDisjoin` functional argument to assay values of overlapping ranges.

    disjoinAssay(ragexp, simplifyDisjoin = mean)

    ##              sample1 sample2
    ## chr1:8-14:+        4      NA
    ## chr1:1-10:-        3       1
    ## chr2:11-14:+      NA       2
    ## chr2:15-18:+       5       2

## 8.4 qreduceAssay

The `qreduceAssay` function works with a `query` parameter that highlights a window of ranges for the resulting matrix. The returned matrix will have dimensions `length(query)` by `ncol(x)`. Elements contain assay values for the i th query range and the j th sample, summarized according to the `simplifyReduce` functional argument.

For demonstration purposes, we can have a look at the original `GRangesList` and the associated scores from which the current `ragexp` object is derived:

    unlist(grl, use.names = FALSE)

    ## GRanges object with 5 ranges and 1 metadata column:
    ##     seqnames    ranges strand |     score
    ##        <Rle> <IRanges>  <Rle> | <integer>
    ##   A     chr1      1-10      - |         3
    ##   B     chr1      8-14      + |         4
    ##   C     chr2     15-18      + |         5
    ##   D     chr1      1-10      - |         1
    ##   E     chr2     11-18      + |         2
    ##   -------
    ##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

This data is represented as `rowRanges` and `assays` in `RaggedExperiment`:

    rowRanges(ragexp)

    ## GRanges object with 5 ranges and 0 metadata columns:
    ##     seqnames    ranges strand
    ##        <Rle> <IRanges>  <Rle>
    ##   A     chr1      1-10      -
    ##   B     chr1      8-14      +
    ##   C     chr2     15-18      +
    ##   D     chr1      1-10      -
    ##   E     chr2     11-18      +
    ##   -------
    ##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

    assay(ragexp, "score")

    ##   sample1 sample2
    ## A       3      NA
    ## B       4      NA
    ## C       5      NA
    ## D      NA       1
    ## E      NA       2

Here we provide the “query” region of interest:

    (query <- GRanges(c("chr1:1-14:-", "chr2:11-18:+")))

    ## GRanges object with 2 ranges and 0 metadata columns:
    ##       seqnames    ranges strand
    ##          <Rle> <IRanges>  <Rle>
    ##   [1]     chr1      1-14      -
    ##   [2]     chr2     11-18      +
    ##   -------
    ##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

The `simplifyReduce` argument in `qreduceAssay` allows the user to summarize overlapping regions with a custom method for the given “query” region of interest. We provide one for calculating a weighted average score per query range, where the weight is proportional to the overlap widths between overlapping ranges and a query range.

Note that there are three arguments to this function. See the documentation for additional details.

    weightedmean <- function(scores, ranges, qranges)
    {
        isects <- pintersect(ranges, qranges)
        sum(scores * width(isects)) / sum(width(isects))
    }

A call to `qreduceAssay` involves the `RaggedExperiment`, the `GRanges` query and the `simplifyReduce` functional argument.

    qreduceAssay(ragexp, query, simplifyReduce = weightedmean)

    ##              sample1 sample2
    ## chr1:1-14:-        3       1
    ## chr2:11-18:+       5       2

See the schematic for a visual representation.

back to top

# 9 Coercion

The `RaggedExperiment` provides a family of parallel functions for coercing to the `SummarizedExperiment` class. By selecting a particular assay index (`i`), a parallel assay coercion method can be achieved.

Here is the list of function names:

- `sparseSummarizedExperiment`
- `compactSummarizedExperiment`
- `disjoinSummarizedExperiment`
- `qreduceSummarizedExperiment`

See the documentation for details.

## 9.1 from dgCMatrix to RaggedExperiment

In the special case where the rownames of a `sparseMatrix` are coercible to `GRanges`, `RaggedExperiment` provides the facility to convert sparse matrices into `RaggedExperiment`. This can be done using the `as` coercion method. The example below first creates an example sparse `dgCMatrix` class and then shows the `as` method usage to this end.

    library(Matrix)

    ##
    ## Attaching package: 'Matrix'

    ## The following object is masked from 'package:S4Vectors':
    ##
    ##     expand

    sm <- Matrix::sparseMatrix(
        i = c(2, 3, 4, 3, 4, 3, 4),
        j = c(1, 1, 1, 3, 3, 4, 4),
        x = c(2L, 4L, 2L, 2L, 2L, 4L, 2L),
        dims = c(4, 4),
        dimnames = list(
            c("chr2:1-10", "chr2:2-10", "chr2:3-10", "chr2:4-10"),
            LETTERS[1:4]
        )
    )

    as(sm, "RaggedExperiment")

    ## class: RaggedExperiment
    ## dim: 7 3
    ## assays(1): counts
    ## rownames: NULL
    ## colnames(3): A C D
    ## colData names(0):

# 10 Session Information

    sessionInfo()

    ## R version 4.4.0 beta (2024-04-15 r86425)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 22.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ## [1] Matrix_1.7-0            RaggedExperiment_1.28.0 GenomicRanges_1.56.0
    ## [4] GenomeInfoDb_1.40.0     IRanges_2.38.0          S4Vectors_0.42.0
    ## [7] BiocGenerics_0.50.0     BiocStyle_2.32.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] jsonlite_1.8.8              highr_0.10
    ##  [3] compiler_4.4.0              BiocManager_1.30.22
    ##  [5] crayon_1.5.2                BiocBaseUtils_1.6.0
    ##  [7] SummarizedExperiment_1.34.0 Biobase_2.64.0
    ##  [9] jquerylib_0.1.4             yaml_2.3.8
    ## [11] fastmap_1.1.1               lattice_0.22-6
    ## [13] R6_2.5.1                    XVector_0.44.0
    ## [15] S4Arrays_1.4.0              knitr_1.46
    ## [17] DelayedArray_0.30.0         bookdown_0.39
    ## [19] MatrixGenerics_1.16.0       GenomeInfoDbData_1.2.12
    ## [21] bslib_0.7.0                 rlang_1.1.3
    ## [23] cachem_1.0.8                xfun_0.43
    ## [25] sass_0.4.9                  SparseArray_1.4.0
    ## [27] cli_3.6.2                   zlibbioc_1.50.0
    ## [29] digest_0.6.35               grid_4.4.0
    ## [31] lifecycle_1.0.4             evaluate_0.23
    ## [33] abind_1.4-5                 rmarkdown_2.26
    ## [35] httr_1.4.7                  matrixStats_1.3.0
    ## [37] tools_4.4.0                 htmltools_0.5.8.1
    ## [39] UCSC.utils_1.0.0

# References

Ramos, Marcel, Martin Morgan, Ludwig Geistlinger, Vincent J Carey, and Levi Waldron. 2023. “RaggedExperiment: the missing link between genomic ranges and matrices in Bioconductor.” Bioinformatics 39 (6): btad330. https://doi.org/10.1093/bioinformatics/btad330.